David R. Anderson – författare

A landmark survey of African American literature about Black experiences of the maritime environment.The ocean is foundational to the story of Africans in America, beginning with the searing Middle Passage. Initially evoking terror, pain, and death, the ocean also became associated with escape, empowerment, freedom, and home, as over time, African Americans in seaport towns found work, built communities, and gained knowledge from travelers. David R. Anderson shows in this groundbreaking collection of memoir, fiction, poetry, and more that African American maritime literature summons many of the traditional themes—survival in the face of overwhelming natural force, sublimity, demonstration of skill and merit, and self-discovery—but often with an eye on legacies of imperialism, slavery, discrimination, and cultural erasure.Divided thematically across ten sections that address peril, labor, recreation, and more, the book gathers work by influential writers and intellectuals from the eighteenth century to the present, including: Lucille Clifton, Edwidge Danticat, Frederick Douglass, Alice Dunbar-Nelson, W. E. B. DuBois, Camille T. Dungy, Marcus Garvey, Robert Hayden, Zora Neale Hurston, Major Jackson, Harriet Jacobs, John S. Jacobs, Nella Larsen, Claude McKay, Toni Morrison, Walter Mosley, Natasha Trethewey, Phillis Wheatley, Colson Whitehead, and Kevin Young.

A landmark survey of African American literature about Black experiences of the maritime environment.The ocean is foundational to the story of Africans in America, beginning with the searing Middle Passage. Initially evoking terror, pain, and death, the ocean also became associated with escape, empowerment, freedom, and home, as over time, African Americans in seaport towns found work, built communities, and gained knowledge from travelers. David R. Anderson shows in this groundbreaking collection of memoir, fiction, poetry, and more that African American maritime literature summons many of the traditional themes—survival in the face of overwhelming natural force, sublimity, demonstration of skill and merit, and self-discovery—but often with an eye on legacies of imperialism, slavery, discrimination, and cultural erasure.Divided thematically across ten sections that address peril, labor, recreation, and more, the book gathers work by influential writers and intellectuals from the eighteenth century to the present, including: Lucille Clifton, Edwidge Danticat, Frederick Douglass, Alice Dunbar-Nelson, W. E. B. DuBois, Camille T. Dungy, Marcus Garvey, Robert Hayden, Zora Neale Hurston, Major Jackson, Harriet Jacobs, John S. Jacobs, Nella Larsen, Claude McKay, Toni Morrison, Walter Mosley, Natasha Trethewey, Phillis Wheatley, Colson Whitehead, and Kevin Young.

The abstract concept of “information” can be quantified and this has led to many important advances in the analysis of data in the empirical sciences. This text focuses on a science philosophy based on “multiple working hypotheses” and statistical models to represent them. The fundamental science question relates to the empirical evidence for hypotheses in this set—a formal strength of evidence. Kullback-Leibler information is the information lost when a model is used to approximate full reality. Hirotugu Akaike found a link between K-L information (a cornerstone of information theory) and the maximized log-likelihood (a cornerstone of mathematical statistics). This combination has become the basis for a new paradigm in model based inference. The text advocates formal inference from all the hypotheses/models in the a priori set—multimodel inference.This compelling approach allows a simple ranking of the science hypothesis and their models. Simple methods are introduced for computing the likelihood of model i, given the data; the probability of model i, given the data; and evidence ratios. These quantities represent a formal strength of evidence and are easy to compute and understand, given the estimated model parameters and associated quantities (e.g., residual sum of squares, maximized log-likelihood, and covariance matrices). Additional forms of multimodel inference include model averaging, unconditional variances, and ways to rank the relative importance of predictor variables.This textbook is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals in various universities, agencies or institutes. Readers are expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameterestimation.

This book is unique in that it covers the philosophy of model-based data analysis and a strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. Kullback-Leibler Information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected to provide an estimate of expected, relative Kullback-Leibler information. This leads to Akaike's Information Criterion (AIC) and various extensions. These are relatively simple and easy to use in practice. The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are objective and practical to employ across a very wide class of empirical problems.Model selection, under the information theoretic approach presented here, attempts to identify the (likely) best model, orders the models from best to worst, and measures the plausibility ("calibration") that each model is really the best as an inference. Model selection methods are extended to allow inference from more than a single "best" model. The book presents several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. This is an applied book written primarily for biologists and statisticians using models for making inferences from empirical data. People interested in the empirical sciences will find this material useful as it offers an alternative to hypothesis testing and Bayesian.

However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (m- timodel inference). S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6.